System and method for automatic mitigation of leaked credentials in computer networks

ABSTRACT

Systems and methods of mitigating leakage of credentials of a user of a computer network, including monitoring at least one data source to scrape data that is compatible with credential data, applying a machine learning algorithm to the scraped data to identify at least one potential leaked credential, wherein the at least one potential leaked credential is identified using at least one neural network, authenticating the identified at least one potential leaked credential by a database of valid credentials of the computer network, and replacing credentials corresponding to the at least one leaked credential.

FIELD OF THE INVENTION

The present invention relates to computer networks. More particularly,the present invention relates to systems and methods for automaticmitigation of leaked credentials in computer networks.

BACKGROUND OF THE INVENTION

Computer networks are constantly vulnerable to malicious attacks byhackers. Some hackers may use social engineering tools or other methodsin order to gather data (e.g., email addresses, passwords, etc.) aboutthe target computer networks. While various cyber security applicationsare available, for instance antivirus software and firewalls, thesesolutions typically cannot prevent attacks where the attacker is inpossession of private information of the computer network. For instance,a user in an organization with a private computer network can post ablog in an external website using the same credentials as used in anaccount of the computer network, such that attackers may steal thesecredentials and attempt to attack the organization with the stolen data.

It can be difficult to monitor all internet traffic for leakedcredentials (e.g., leaked passwords) and/or act to mitigate the threatthey pose. To monitor the leaked credentials, it can be possible toparse and/or store leaked databases to prevent hacking operations. Manyorganizations in the world have been hacked, and their database leakedby the hackers. Those databases can consist of valuable data such ascredentials. Downloading such databases, parsing them, and/or storingthe precious data is one typically used approach for identifying leakedcredentials. Another typical approach is to monitor malicious sources tolook for credentials, where these sources can be known for publishingleaked data along legitimate data.

Currently, one approach for mitigating the threat is to take the leakedpasswords, identify the persons behind them within the organization, andask them to change their password (and/or enforce a password change).This can result in a process consuming time of the technical stuff, asit is typically not automated.

SUMMARY OF THE INVENTION

There is thus provided, in accordance with some embodiments of theinvention, a method of mitigating leakage of credentials of a user of acomputer network, the method including monitoring, by a processor incommunication with the computer network, at least one data source toscrape data that is compatible with credential data, applying a machinelearning algorithm to the scraped data, by the processor, to identify atleast one potential leaked credential, wherein the at least onepotential leaked credential is identified using at least one neuralnetwork, authenticating, by an active directory application of thecomputer network, the identified at least one potential leakedcredential by a database of valid credentials of the computer network,and replacing credentials corresponding to the at least one leakedcredential, by the active directory application.

In some embodiments, the machine learning algorithm is trained on apredetermined set of credentials to identify credentials in anunstructured text. In some embodiments, the machine learning algorithmis configured to identify credentials in chunks of the unstructuredtext.

In some embodiments, the at least one potential leaked credential ischecked to correspond to at least one predetermined domain name of thecomputer network. In some embodiments, the at least one data source isexternal to the computer network. In some embodiments, the scraping ofdata is carried out periodically at predetermined times.

In some embodiments, an alert is issued to the user corresponding to theauthenticated at least one leaked credential. In some embodiments, theauthenticated at least one leaked credential is removed from the atleast one data source. In some embodiments, irrelevant credentials arefiltered out from the identified at least one potential leakedcredential in accordance with the active directory application.

There is thus provided, in accordance with some embodiments of theinvention, a system for mitigation of leakage of credentials of a userof a computer network, the system including a database of validcredentials of the computer network, an active directory application ofthe computer network, and a processor, in communication with at leastone data source, wherein the processor is configured to: monitor the atleast one data source, and apply a machine learning algorithm to themonitored data to identify at least one potential leaked credential. Insome embodiments, the at least one potential leaked credential isidentified using at least one neural network. In some embodiments, theactive directory application is configured to: authenticate theidentified at least one potential leaked credential by the database ofvalid credentials, and replace credentials corresponding to the at leastone leaked credential.

In some embodiments, the machine learning algorithm is trained on apredetermined set of credentials to identify credentials in anunstructured text. In some embodiments, the machine learning algorithmis configured to identify credentials in chunks of the unstructuredtext. In some embodiments, the processor is configured to check if theat least one potential leaked credential corresponds to at least onepredetermined domain name of the computer network. In some embodiments,the at least one data source is external to the computer network. Insome embodiments, the processor is configured to scrape dataperiodically at predetermined times. In some embodiments, the processoris configured to issue an alert to the user corresponding to theauthenticated at least one leaked credential. In some embodiments, theprocessor is configured to remove the authenticated at least one leakedcredential from the at least one data source. In some embodiments, theprocessor is configured to filter out irrelevant credentials from theidentified at least one potential leaked credential in accordance withthe active directory application.

There is thus provided, in accordance with some embodiments of theinvention, a method of mitigating leakage of credentials of a user of acomputer network, the method including monitoring, by a processor incommunication with the computer network, at least one data source toscrape data compatible with credential data, applying a machine learningalgorithm to the scraped data, by the processor, to identify at leastone potential leaked credential, wherein the at least one potentialleaked credential is identified using at least one neural network,authenticating, by of the computer network, the identified at least onepotential leaked credential by a database of valid credentials of thecomputer network, and removing the authenticated at least one leakedcredential from the at least one data source. In some embodiments,credentials corresponding to the at least one leaked credential arereplaced by an active directory application of the computer network.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings in which:

FIG. 1 shows a block diagram of a computing device, according to someembodiments of the invention;

FIG. 2 shows a block diagram of a system for mitigation of leakage ofcredentials of a user of a computer network, according to someembodiments of the invention;

FIG. 3 shows a flowchart for a method of training a machine learningalgorithm to identify credentials, according to some embodiments of theinvention; and

FIG. 4 shows a flowchart for a method of mitigating leakage ofcredentials of a user of a computer network, according to someembodiments of the invention.

It will be appreciated that, for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

DETAILED DESCRIPTION OF THE INVENTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresent invention may be practiced without these specific details. Inother instances, well-known methods, procedures, and components have notbeen described in detail so as not to obscure the present invention.

Although embodiments of the invention are not limited in this regard,discussions utilizing terms such as, for example, “processing,”“computing,” “calculating,” “determining,” “establishing”, “analyzing”,“checking”, or the like, may refer to operation(s) and/or process(es) ofa computer, a computing platform, a computing system, or otherelectronic computing device, that manipulates and/or transforms datarepresented as physical (e.g., electronic) quantities within thecomputer's registers and/or memories into other data similarlyrepresented as physical quantities within the computer's registersand/or memories or other information non-transitory storage medium thatmay store instructions to perform operations and/or processes. Althoughembodiments of the invention are not limited in this regard, the terms“plurality” and “a plurality” as used herein may include, for example,“multiple” or “two or more”. The terms “plurality” or “a plurality” maybe used throughout the specification to describe two or more components,devices, elements, units, parameters, or the like. Unless explicitlystated, the method embodiments described herein are not constrained to aparticular order or sequence. Additionally, some of the described methodembodiments or elements thereof can occur or be performedsimultaneously, at the same point in time, or concurrently.

According to some embodiments, systems and methods are provided forautomatic mitigation of leaked credentials in computer networks.External data sources (e.g., websites where hackers publish stolen data)may be monitored to retrieve potential leaked credentials andauthenticate and/or validate integrity of the retrieved data bycomparison to internal credential data of the computer network to verifythat the credentials were indeed leaked. Finally, the leaked credentialsmay be mitigated by automatically replacing the correspondingcredentials in the computer network and/or removing the publication inthe relevant external data source. If such a process was to be carriedout by a human analyst, the result would be a process that constantlychanges based on the particular human, and some humans may make errors(e.g., fail to enforce the credential change, miss a leaked credentialwithin unstructured text, etc.). Therefore, automation of mitigatingleaked credentials is an advantage that may eliminate the human error.

Reference is made to FIG. 1, showing a block diagram of an exemplarycomputing device, according to some embodiments of the presentinvention. Computing device 100 may include a controller 105 that maybe, for example, a central processing unit processor (CPU), a chip orany suitable computing or computational device, an operating system 115,a memory 120, a storage 130, input devices 135 and output devices 140.Controller 105 may be configured to carry out methods as disclosedherein by for example executing code or software.

Operating system 115 may be or may include any code segment designedand/or configured to perform tasks involving coordination, scheduling,arbitration, supervising, controlling or otherwise managing operation ofcomputing device 100, for example, scheduling execution of programs.Operating system 115 may be a commercial operating system. Memory 120may be or may include, for example, a Random Access Memory (RAM), a readonly memory (ROM), a Dynamic RAM (DRAM), a Synchronous DRAM (SD-RAM), adouble data rate (DDR) memory chip, a Flash memory, a volatile memory, anon-volatile memory, a cache memory, a buffer, a short term memory unit,a long term memory unit, or other suitable memory units or storageunits. Memory 120 may be or may include a plurality of, possiblydifferent memory units.

Executable code 125 may be any executable code, e.g., an application, aprogram, a process, task or script. Executable code 125 may be executedby controller 105 possibly under control of operating system 115. Forexample, executable code 125 may be an application for monitoringinteractions in computer systems. Where applicable, executable code 125may carry out operations described herein in real-time. Computing device100 and executable code 125 may be configured to update, process and/oract upon information at the same rate the information, or a relevantevent, are received. In some embodiments, more than one computing device100 may be used. For example, a plurality of computing devices thatinclude components similar to those included in computing device 100 maybe connected to a network and used as a system.

Storage 130 may be or may include, for example, a hard disk drive, aCompact Disk (CD) drive, a CD-Recordable (CD-R) drive, a universalserial bus (USB) device or other suitable removable and/or fixed storageunit. Data may be stored in storage 130 and may be loaded from storage130 into memory 120 where it may be processed by controller 105. In someembodiments, some of the components shown in FIG. 1 may be omitted. Forexample, memory 120 may be a non-volatile memory having the storagecapacity of storage 130. Accordingly, although shown as a separatecomponent, storage 130 may be embedded or included in memory 120.

Input devices 135 may be or may include a mouse, a keyboard, a touchscreen or pad or any suitable input device. It will be recognized thatany suitable number of input devices may be operatively connected tocomputing device 100 as shown by block 135. Output devices 140 mayinclude one or more displays, speakers and/or any other suitable outputdevices. It will be recognized that any suitable number of outputdevices may be operatively connected to computing device 100 as shown byblock 140. Any applicable input/output (I/O) devices may be connected tocomputing device 100 as shown by blocks 135 and 140. For example, awired or wireless network interface card (NIC), a modem, printer orfacsimile machine, a universal serial bus (USB) device or external harddrive may be included in input devices 135 and/or output devices 140.

Some embodiments of the invention may include an article such as acomputer or processor non-transitory readable medium, or a computer orprocessor non-transitory storage medium, such as for example a memory, adisk drive, or a USB flash memory, encoding, including or storinginstructions, e.g., computer-executable instructions, which, whenexecuted by a processor or controller, cause the processor to carry outmethods disclosed herein. For example, some embodiments of the inventionmay include a storage medium such as memory 120, computer-executableinstructions such as executable code 125 and a controller such ascontroller 105.

A computer or processor non-transitory storage medium, may include forexample a memory, a disk drive, or a USB flash memory, encoding,including or storing instructions, e.g., computer-executableinstructions, which when executed by a processor or controller, carryout methods disclosed herein. The storage medium may include, but is notlimited to, any type of disk including floppy disks, optical disks,compact disk read-only memories (CD-ROMs), rewritable compact disk(CD-RWs), and magneto-optical disks, semiconductor devices such asread-only memories (ROMs), random access memories (RAMs), such as adynamic RAM (DRAM), erasable programmable read-only memories (EPROMs),flash memories, electrically erasable programmable read-only memories(EEPROMs), magnetic or optical cards, or any type of media suitable forstoring electronic instructions, including programmable storage devices.

In some embodiments, a system may include or may be, for example, apersonal computer, a desktop computer, a mobile computer, a laptopcomputer, a notebook computer, a terminal, a workstation, a servercomputer, a Personal Digital Assistant (PDA) device, a tablet computer,a network device, or any other suitable computing device. Unlessexplicitly stated, the method embodiments described herein are notconstrained to a particular order or sequence. Additionally, some of thedescribed method embodiments or elements thereof can occur or beperformed at the same point in time.

Reference is now made to FIG. 2, which shows a block diagram of a system200 for mitigating leakage of credentials of a user of a computernetwork 210, according to some embodiments. A user of computer network210 may be any person using a computing device of computer network 210.Computer network 210, for instance a computer network of anorganization, may include at least one computing device 211 (e.g.,similar to the computing device 100 shown in FIG. 1) in communicationwith at least one server 212. The system 200 may include a processor 201(e.g., similar to computing device 100 shown in FIG. 1) in communicationwith the computer network 210, for instance communicating via at leastone server 212. In some embodiments, the direction of arrows in FIG. 2indicates the direction of information flow. In some embodiments,hardware elements of system 200 are indicated with a solid line in FIG.2.

The system 200 may include a database 202 (e.g., similar to storage 130as shown in FIG. 1) coupled to the processor 201 (e.g., connected via awired/wireless connection), with the database 202 storing validcredentials (e.g., email addresses, usernames and/or passwords) of thecomputer network 210. In some embodiments, the system 200 includes anactive directory application 213 of the computer network 210, the activedirectory application 213 coupled to the processor 201 and configured tomanage credentials within the computer network 210. In some embodiments,the system 200 includes a virtual appliance embedded within the computernetwork 210 to create an interface between the active directoryapplication 213 and the processor 201.

According to some embodiments, the processor 201 is in communicationwith at least one external data source 203, such as an external website(not shown) where hackers post stolen credentials. The at least oneexternal data source 203 may include leaked data published by hackersthat maliciously extracted data (not only credentials) from differenthacked organizations, for instance an organization external to thecomputer network 210. Data retrieved or extracted from the at least oneexternal data source 203 may include at least one potential leakedcredential 205 related to the computer network 210, for instance storedat a dedicated potential leaked credentials database. For example, incase that a large database is leaked (e.g., a database of Amazon'scustomers) and employees of the organization operating the computernetwork 210 have users with credentials stored in the leaked database,then the credentials of the computer network 210 may also be at risk.

The at least one external data source 203, in some embodiments, includessources that are dedicated to publishing malicious content. Such sources(e.g., websites) may be particularly used by hackers to publish leakeddata that they extracted, when the data source maintains anonymity forthe publishers. The processor 201 may initiate scraping of the at leastone external data source 203 to extract any leaked data as soon as it ispublished, and for instance storing scraped data 204 at a dedicatedscraped data database for future analysis (e.g., to extractcredentials). In some embodiments, scraping of data is carried outperiodically at predetermined times (such as for example, every week,once a month, once a day, and/or every hour).

It should be noted that (raw) data not yet processed in at least oneexternal data source 203 may include various types of data and not onlycredentials, for example a textual document with various informationincluding credentials therein. The processor 201 may identify andextract only credentials from the data in the at least one external datasource 203, for instance extract passwords from a long textual passage,e.g., by applying a machine learning algorithm as further detailedbelow.

According to some embodiments, the processor 201 applies a machinelearning algorithm 206 on (monitored) data of the at least one datasource 203 in order to identify at least one potential leaked credential205 using at least one neural network (NN) 207, for instance monitoringthe at least one data source 203 to retrieve potential credentials. Aneural network (e.g., NN implementing machine learning) may refer to aninformation processing paradigm that may include nodes, referred to asneurons, organized into layers, with links between the neurons. Thelinks may transfer signals between neurons and may be associated withweights. A NN may be configured or trained for a specific task, e.g.,pattern recognition or classification. Training a NN for the specifictask may involve adjusting these weights based on examples. Each neuronof an intermediate or last layer may receive an input signal, e.g., aweighted sum of output signals from other neurons, and may process theinput signal using a linear or nonlinear function (e.g., an activationfunction). The results of the input and intermediate layers may betransferred to other neurons and the results of the output layer may beprovided as the output of the NN. Typically, the neurons and linkswithin a NN are represented by mathematical constructs, such asactivation functions and matrices of data elements and weights. Aprocessor, e.g. CPUs or graphics processing units (GPUs), or a dedicatedhardware device may perform the relevant calculations.

The machine learning algorithm 206 may be initially trained to identifycredentials from input data (e.g., a text including credentials as wellas non-credentials). For example, if the machine learning algorithm 206receives the text “tiger” or “pass123456” as input, the output may be todetermine what is the probability of that text to be a credential, suchas a password.

Reference is now made to FIG. 3, which shows a flowchart for a method oftraining a machine learning algorithm to identify credentials, accordingto some embodiments. The machine learning algorithm 206 may be trainedby feeding 301 a plurality of known credentials (e.g., known credentialsthat may have been previously leaked and/or credentials as input by auser) such as validated passwords and/or email addresses, etc., to theat least one neural network 207 until the pattern and/or structure ofcredentials is learned. Thus, a machine learning model may be created,for instance trained with a neural network as is known in the art, wheresimilarity to credentials may be determined for an unknown and/orunstructured input (e.g., text).

In case that the input includes unstructured text 302, the unstructuredtext 302 may be parsed 303 into groups of text (e.g., chunks of text)such as terms within a sentence, that potentially include at least onecredential therein. Unstructured text, for example user-generatedinformation such as emails or social media postings, may be any writtencontent (without metadata), for example content written by a human userin a word processor program and/or any content readable by a human user,not organized in a pre-defined manner and/or may not be indexed ontostandard database fields.

In some embodiments, determining that a chunk of text includes at leastone credential, involves determining that at least one potentialcredential identified 304 within the chunk of text corresponds to acontext of credentials. The context of text, within the chunk of text,can be the line of text previous to the potential credential and alsothe line of text following the potential credential such that analysisof the entire passage may determine the appropriate context of the text(e.g., being a potential credential or not), for instance using knownalgorithms. In some embodiments, identified potential credential samplesare tagged (e.g., by another computing program and/or human analyst) inorder to improve the identification by the machine learning algorithm206.

For example, “Password1!” may resemble a password, but it may be in twodifferent contexts. The first context can result in the term “Password!”being an actual password, a second context can result in the term“Password!” as being a phrase or other form of text. If the potentialcredential is for instance within the sentence: “It is known thatPassword1! is one of the most common passwords in the world”, it may notbe identified as a leaked credential, while for the sentence: “User:admin, Password: Password1!” may be identified as a leaked credential.

In some embodiments, the input includes regular text (e.g., withoutunstructured text 302), and the machine learning algorithm 206 maydirectly extract 305 at least one potential credential. Once at leastone potential credential is extracted and/or identified, it may be added306 to a dataset of credentials for future use by the machine learningalgorithm 206.

Reference is now made back to FIG. 2, according to some embodiments thesystem 200 identifies assets of the computer network 210 (e.g., assetsof the organization operating the computer network 210) among at leastone potential leaked credential 205. The processor 201 may check if atleast one potential leaked credential 205 corresponds to at least onepredetermined domain name of computer network 210. For example, if thesystem 200 is to protect the organization “Roogle” operating thecomputer network 210 including their domain “roogle.com”, then theprocessor 201 may identify users of that domain in order to identifyrelated leaked credentials, for example identifying leaked passwords fora user with an email address “bob@roogle.com”.

In some embodiments, details of users of the computer network 210 arestored at the database 202 in order compare the potential leakedcredentials 205 with data of those users.

According to some embodiments, the system 200 identifies at least onepotential leaked credential 205, classify the identified at least onepotential leaked credential 205 into relevant (e.g., corresponding tothe computer network 210) and irrelevant credentials, and/or mitigatethreats from leaked credentials. In some embodiments, the activedirectory application 213 replaces credentials corresponding to the atleast one leaked credential (e.g., automatically change passwords withpredefined replacement password instead of the leaked passwords) tomitigate the threat caused by the leaked credentials. The processor 201may filter out irrelevant credentials from the identified at least onepotential leaked credential 205 in accordance with the active directoryapplication 213.

Upon determination of at least one potential leaked credential 205, theactive directory application 213 may authenticate at least one potentialleaked credential 205 by the database 202 of valid credentials, forinstance by checking that the passwords of the computer network 210 arecompatible to the potential leaked credential and/or checking that thepasswords are still valid. In case that the active directory application213 does not authenticate at least one potential leaked credential 205,there may be no need to issue an alert. In case that active directoryapplication 213 authenticates the at least one potential leakedcredential 205, an alert may be issued to a predefined party, forinstance to the security administrator and/or the user of the computernetwork 210. In some embodiments, if the active directory application213 authenticates the at least one potential leaked credential 205, theat least one leaked credential is automatically reset (e.g.,automatically reset a password at the active directory application 213).

If the active directory application 213 authenticates at least onepotential leaked credential 205, the system 200 may externally mitigatethe threat, e.g., being carried out externally to the system 200, byremoving and/or blocking the leaked data at the at least one externaldata source 203 (e.g., send a request to a social media website toremove a malicious publication). In some embodiments, the system 200externally mitigates the threat by blocking communication to and/or fromleaked credentials (e.g., blocking access to a hacked email address)with corresponding instructions to the perimeter of the computernetwork, for instance the firewall and/or mail gateway and/or web proxyand/or a virtual private network (VPN) server. In some embodiments, forcomputer networks of large organizations, the mail gateway is connectedto the active directory application, such that the same credential(e.g., password or email) works as well as on each user's PC or laptop.

Reference is now made to FIG. 4, which shows a flowchart of a method ofmitigating leakage of credentials of a user of a computer network (210in FIG. 2), according to some embodiments. In step 401, a processor(such as processor 201 in FIG. 2), in communication with computernetwork 210, may monitor at least one data source (203 in FIG. 2) toscrape data that is compatible with credential data (e.g., passwordsand/or usernames and/or email addresses). According to some embodiments,in step 402, processor 201 applies a machine learning algorithm (206 inFIG. 2) to scraped data 204 to identify at least one potential leakedcredential 205, wherein the at least one potential leaked credential isidentified using at least one neural network (207 in FIG. 2).

In some embodiments, in step 403, active directory application (213 inFIG. 2), of the computer network 210, authenticates the identified atleast one potential leaked credential 205 by a database 202 of validcredentials of the computer network 210. To mitigate the threat, in step404, the processor 201 may instruct the active directory application 213to replace credentials corresponding to the at least one leakedcredential 205 and/or remove the leaked data from the at least oneexternal data source 203.

In some embodiments, the system 200 improves computer security bymitigating leakage of credentials thereby improving computer networktechnology. The system 200 also improves the technological problem ofextracting leaked credentials from unstructured sources, such as freetexts. The system 200 may apply a dedicated machine learning algorithmto identify potential leaked credentials within any text to be extractedfor mitigation. The system 200 may also provide immediate response toblock the leaked data (e.g., after validation of the extracted potentialleaked credentials) due to the automation of the process, since there isno need to wait for an action by a human administrator. In someembodiments, integration of the system 200 into any computer networkallows automatic mitigation of leaked credentials thereby preventingmalicious attacks on the network that can take advantage of leakedcredentials.

Unless explicitly stated, the method embodiments described herein arenot constrained to a particular order in time or chronological sequence.Additionally, some of the described method elements can be skipped, orthey can be repeated, during a sequence of operations of a method.

Various embodiments have been presented. Each of these embodiments mayof course include features from other embodiments presented, andembodiments not specifically described may include various featuresdescribed herein.

1. A method of mitigating leakage of credentials of a user of a computernetwork, the method comprising: monitoring, by a processor incommunication with the computer network, at least one data source toscrape data that is compatible with credential data; applying a machinelearning algorithm to the scraped data, by the processor, to identify atleast one potential leaked credential, wherein the at least onepotential leaked credential is identified using at least one neuralnetwork; authenticating, by an active directory application of thecomputer network, the identified at least one potential leakedcredential by a database of valid credentials of the computer network;and replacing credentials corresponding to the at least one leakedcredential, by the active directory application.
 2. The method of claim1, further comprising training the machine learning algorithm on apredetermined set of credentials to identify credentials in anunstructured text.
 3. The method of claim 2, wherein the machinelearning algorithm is configured to identify credentials in chunks ofthe unstructured text.
 4. The method of claim 1, further comprisingchecking if the at least one potential leaked credential corresponds toat least one predetermined domain name of the computer network.
 5. Themethod of claim 1, wherein the at least one data source is external tothe computer network.
 6. The method of claim 1, wherein the scraping ofdata is carried out periodically at predetermined times.
 7. The methodof claim 1, further comprising issuing an alert to the usercorresponding to the authenticated at least one leaked credential. 8.The method of claim 1, further comprising removing the authenticated atleast one leaked credential from the at least one data source.
 9. Themethod of claim 1, further comprising filtering out irrelevantcredentials from the identified at least one potential leaked credentialin accordance with the active directory application.
 10. A system formitigation of leakage of credentials of a user of a computer network,the system comprising: a database of valid credentials of the computernetwork; an active directory application of the computer network; and aprocessor, in communication with at least one data source; wherein theprocessor is configured to: monitor the at least one data source; andapply a machine learning algorithm to the monitored data to identify atleast one potential leaked credential, wherein the at least onepotential leaked credential is identified using at least one neuralnetwork; and wherein the active directory application is configured to:authenticate the identified at least one potential leaked credential bythe database of valid credentials; and replace credentials correspondingto the at least one leaked credential.
 11. The system of claim 10,wherein the machine learning algorithm is trained on a predetermined setof credentials to identify credentials in an unstructured text.
 12. Thesystem of claim 11, wherein the machine learning algorithm is configuredto identify credentials in chunks of the unstructured text.
 13. Thesystem of claim 10, wherein the processor is configured to check if theat least one potential leaked credential corresponds to at least onepredetermined domain name of the computer network.
 14. The system ofclaim 10, wherein the at least one data source is external to thecomputer network.
 15. The system of claim 10, wherein the processor isconfigured to scrape data periodically at predetermined times.
 16. Thesystem of claim 10, wherein the processor is configured to issue analert to the user corresponding to the authenticated at least one leakedcredential.
 17. The system of claim 10, wherein the processor isconfigured to remove the authenticated at least one leaked credentialfrom the at least one data source.
 18. The system of claim 10, whereinthe processor is configured to filter out irrelevant credentials fromthe identified at least one potential leaked credential in accordancewith the active directory application.
 19. A method of mitigatingleakage of credentials of a user of a computer network, the methodcomprising: monitoring, by a processor in communication with thecomputer network, at least one data source to scrape data compatiblewith credential data; applying a machine learning algorithm to thescraped data, by the processor, to identify at least one potentialleaked credential, wherein the at least one potential leaked credentialis identified using at least one neural network; authenticating, by ofthe computer network, the identified at least one potential leakedcredential by a database of valid credentials of the computer network;and removing the authenticated at least one leaked credential from theat least one data source.
 20. The method of claim 1, further comprisingreplacing credentials corresponding to the at least one leakedcredential, by an active directory application of the computer network.